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Deep belief networks based radar signal classification system

  • Chang Min Jeong
  • Young Giu Jung
  • Sang Jo Lee
Original Research
  • 45 Downloads

Abstract

A threat library is used in most of the existing electronic warfare systems to identify or execute jamming against various radar signals. The conventional method uses frequency, pulse repetition interval, and pulse width sampled from the pulse description word column as characteristics of a signal. Such sampling technique cannot effectively model each radar signal when dealing with a complex signal array. In this paper, a new deep belief network model is proposed to generate a more efficient threat library for radar signal classification. The proposed model consists of independent restricted Boltzman machines (RBMs) of frequency, pulse repetition interval, pulse width respectively, and a RBM which fuses the result again. The performance of the existing system and the proposed system is evaluated by testing the signals with measurement errors and insufficient information. As a result, the proposed system shows more than 6% performance improvement over the existing system.

Keywords

Threat library DBN BP Radar signal classification 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Agency for Defense DevelopmentDaejeonRepublic of Korea
  2. 2.YM-NaeultechIncheonRepublic of Korea
  3. 3.Department of Computer EngineeringKyungpook National UniversityDaeguRepublic of Korea

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